Learning with multiple agents:
Many approaches related to learning with multiple agents exist. One of them is called coactive learning [1]}Different from coactive learning in [2]}.. It involves developing a distributed learning system to learn on large labeled datasets over multiple networks. They have a single domain and multiple `coactors' learning from that domain in parallel. The learning dataset is divided among the coactors. The communication is through transfer of misclassified training instances between the coactors. The focus of their approach is to parallelize the learning for a gain in speed. Our setting is different in that the different agents are learning in different environments and are communicating with the goal of improving accuracy. Collaborative machine learning leverages multiple sources of data (from a community of users) for information retrieval [3]} or recommendation tasks [4]}. In Siamese Neural Networks [5]}, [6]}, multiple layers of neurons share weights whereas our approach involves multiple agents sharing category models in a semantic mid-level feature (attribute)space. Multi-task learning [7]}, [8]} aims to optimize performance on multiple tasks, such as recognizing cats and dogs, by exploiting the relatedness of the tasks. In our setting, the tasks are the same, but the sources of data are different. Multi-view learning [9]} involves using different views (features) for a single task. But these views are aligned two different features extracted from the same image. Similarly in co-training or co-adaptation [10]}, [11]}, there are two views of the same data. A model is trained for each view. Each model identifies unlabeled images that it is most confident of. These images are fed to the other model as new training data annotated with the labels predicted by the first model. Again, this is feasible because the data is assumed to be aligned, just as in multi-view learning. Recent work on multi-modal learning [12]}, [13]}, [14]}, [15]} using deep networks also assumes that the data is aligned. In our setting, there is no alignment in the sources of data being used by both agents to learn. Finally, “co-robots” [16]} refer to robots that are being trained to work along side humans. Our setting involves interactions between two machines without human involvement. A high-level description of ideas related to cooperative learning (without a discussion of modes of communication, algorithms, or implementation) is presented in [17]}.
